{"id":"W2009631040","doi":"10.1016/j.conengprac.2009.09.011","title":"Industrial implementation of controller performance analysis technology","year":2009,"lang":"en","type":"article","venue":"Control Engineering Practice","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Syncrude (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Syncrude","keywords":"Toolbox; Identification (biology); Univariate; Controller (irrigation); Computer science; Control (management); Engineering; Control engineering; Multivariate statistics; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003203016,0.0001685932,0.0004125192,0.0005817497,0.00003136716,0.00002626708,0.0001314685,0.0001514581,0.00002841548],"category_scores_gemma":[0.000158031,0.0001761851,0.0001154992,0.001066916,0.00000999008,0.0003216451,0.000004641436,0.0002514984,0.00001289394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006696163,"about_ca_system_score_gemma":0.00001553854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001540667,"about_ca_topic_score_gemma":0.000002438711,"domain_scores_codex":[0.9989533,0.00002562948,0.0004402188,0.0001460097,0.000183769,0.0002510793],"domain_scores_gemma":[0.999387,0.0001349636,0.0001140141,0.000203168,0.0001039484,0.00005686553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007967713,0.0000198381,0.00058188,0.00001430387,0.001101144,0.000002652204,0.00005859082,0.9405787,0.01273253,0.0004536075,0.00008666401,0.04429042],"study_design_scores_gemma":[0.00394577,0.0001843707,0.003954076,0.00001188042,0.0007749224,0.00001417456,0.0001802914,0.9719072,0.002093777,0.000003965087,0.0167138,0.0002157501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4815171,0.002155517,0.5036968,0.002239004,0.001950187,0.001703021,0.00004629796,0.002709701,0.003982377],"genre_scores_gemma":[0.999491,0.00003291554,0.0002146771,0.00005927291,0.0001261579,0.00003746194,0.000002897195,0.00001607552,0.00001947773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.517974,"threshold_uncertainty_score":0.7184618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006386718316162067,"score_gpt":0.2385516949598162,"score_spread":0.2321649766436541,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}